Based on our experience running AIMultiple’s cloud GPU benchmark with 10 different GPU models in 4 different scenarios, these are the top AI hardware companies for data center workloads. Follow the links to see our rationale behind each selection:
25+ AI chip makers by category
*The selected models are based on the latest announcements.
**ACCEL was developed by Chinese scientists in collaboration with Alibaba and China’s Semiconductor Manufacturing International Corporation (SMIC)
Sorting is by category. Vendors are ranked by estimated market share within the top 3 categories (i.e., leading producer, public cloud, public AI cloud) because sales numbers or cloud usage can be estimated. Vendors in the last three categories (i.e., AI startup, upcoming producer, other producers) are sorted alphabetically.
5 mobile AI chip providers
*Most popular & recent chips are selected.
5 edge AI chips
The demand for low-latency processing has driven innovation in edge AI chips. These chips’ processors are designed to perform AI computations locally on devices rather than relying on cloud-based solutions:
*These are the maximum quoted values by the vendors. TOPS is tera operations per second.
Understanding AI chip architectures: GPUs vs ASICs
Not all AI chips are created equal. While the vendors above compete in the same market, they use fundamentally different chip architectures:
- GPUs (Graphics Processing Units) are general-purpose processors that can handle both training and inference across a wide range of AI workloads. NVIDIA and AMD dominate this category.
- ASICs (Application-Specific Integrated Circuits) are custom-designed for specific tasks. Some support both training and inference (Google TPU, AWS Trainium), while others are inference-only (Groq LPU, AWS Inferentia).
Key insight:
Not all ASICs are inference-only. Google TPU, AWS Trainium, Cerebras, and SambaNova support both training and inference, while Groq LPU and AWS Inferentia focus exclusively on inference.
This distinction matters for buyers: GPUs offer flexibility across different AI workloads, while ASICs deliver better performance per watt but are harder to reprogram as model architectures change.
According to TrendForce, based on AI server shipment growth rates, custom ASIC shipments from cloud providers are projected to grow 44.6% in 2026, while GPU shipments are expected to grow 16.1%. This signals a shift in the AI hardware landscape, with hyperscalers increasingly investing in their own silicon.
Which are the leading AI chip producers?
1. NVIDIA
NVIDIA has been designing graphics processing units (GPUs) for the gaming sector since the 1990s. NVIDIA is a fabless chip manufacturer that outsources most of its chip manufacturing to TSMC. Its main businesses include:
Desktop AI solutions
DGX Spark (formerly Project Digits) is a desktop AI supercomputer for AI engineers and data scientists that is:
- Expected to cost around $3k.
- It is about the same size as a Mac mini and powered by the NVIDIA GB10 Grace Blackwell Superchip with 128GB memory.
- Capable of handling LLM inference and fine-tuning for models with up to 200 billion parameters, leveraging NVLink-C2C for high-speed CPU+GPU communication.
Datacenter solutions
The company makes AI chips following its Ampere, Hopper, and, most recently, Blackwell architectures. Thanks to the generative AI boom, NVIDIA had excellent results in the past years, reached a trillion in valuation, and solidified its status as the leader of the GPU and AI hardware markets. The following chart shows how NVIDIA’s revenue in this segment has grown over the years and how it has become the company’s primary source of income.
NVIDIA’s chipsets are designed to solve business problems in various industries. DGX™ A100 and H100 have been successful flagship AI chips of Nvidia, designed for AI training and inference in data centers. NVIDIA followed up on these with
- H200, B300 and GB300 chips
- HGX servers such as HGX H200 and HGX B300 that combine 8 of these chips
- NVL series and GB200 SuperPod that combine even more chips into large clusters.
Cloud GPUs
Thanks to the strength of its datacenter offering, NVIDIA almost has a monopoly on the cloud AI market, with most cloud players offering only NVIDIA GPUs as cloud GPUs.
NVIDIA also launched its DGX Cloud offering, providing cloud GPU infrastructure directly to enterprises, bypassing cloud providers.
GPUs for graphics
Xbox uses a chipset co-developed by NVIDIA and Microsoft. NVIDIA’s GPUs for retail users include the GeForce series.
Recent developments
DGX Cloud Lepton
Announced on May 19, 2025, at Computex, NVIDIA’s DGX Cloud Lepton is a marketplace that connects AI developers to NVIDIA’s GPU cloud providers, including CoreWeave, Lambda, and Crusoe. It enables flexible access to GPU resources for AI model training and inference, bypassing traditional cloud provider dependencies. This strengthens NVIDIA’s enterprise-focused cloud strategy.
NVIDIA Dynamo
NVIDIA Dynamo, announced at GTC 2025, is a new open-source inference framework designed for high-throughput, low-latency deployment of generative AI models in distributed environments, boosting request serving by up to 30x on NVIDIA Blackwell as shown in the figure below. This framework, compatible with popular tools like PyTorch and TensorRT-LLM, utilizes innovations such as disaggregated inference stages and dynamic GPU scheduling to optimize performance and reduce costs. Available on GitHub for developers and included in NVIDIA NIM microservices for enterprise solutions, Dynamo facilitates scalable and cost-effective generative AI serving from single to multi-GPU systems.
NVIDIA RTX PRO Servers and Enterprise AI Factory
Announced in May 2025 at Computex, NVIDIA introduced RTX PRO Servers powered by RTX PRO 6000 Blackwell Server Edition GPUs, designed for enterprise AI factories. These servers deliver universal acceleration for AI, design, engineering, and business applications, supporting workloads like multimodal AI inference, physical AI, and digital twins on the NVIDIA Omniverse platform.
The NVIDIA Enterprise AI Factory validated design, incorporating RTX PRO Servers, NVIDIA Spectrum-X Ethernet, NVIDIA BlueField DPUs, and NVIDIA AI Enterprise software, enables partners like Cadence, Foxconn, and Lilly to build on-premises AI infrastructure. This initiative accelerates the trillion-dollar IT industry transition to GPU-accelerated AI factories.
DeepSeek
Release of DeepSeek’s R1 showed that state-of-the-art models could be trained with a relatively small number of GPUs. This led to a reduction in NVIDIA’s stock price. Though this is not investment advice, this can be positive for NVIDIA since the more utility computing power provides, the more widely it should be used (i.e., Jevons paradox).
However, given that GPU systems’ performance improves multiple times annually due to advances in chip design and interconnect, buyers would be wise not to buy beyond their annual needs, as this can lead to owning outdated systems.
Tariffs & export restrictions
NVIDIA is now permitted to export advanced AI processors to the Chinese market, marking a shift from previous requirements to sell only downgraded versions. However, these exports face new logistical and financial hurdles: chips manufactured in Taiwan must now detour through the United States for third-party testing, triggering a newly imposed 25% national security tariff.
Despite the restored access to high-end hardware, the added costs and supply chain complexities continue to incentivize the Chinese government and chip industry to develop competitive local alternatives. While Chinese chips currently underperform NVIDIA’s latest technology, these trade barriers ensure that domestic development remains a strategic priority, potentially challenging NVIDIA’s market dominance in the future.
Inference Market Competition
While NVIDIA dominates the AI “training” market, competition is heating up in “inference,” the deployment of AI models for real-world tasks. Companies like AMD and numerous startups, including Untether AI and Groq, are developing chips that aim to provide more cost-effective inference solutions, with a particular focus on lower power consumption.
New “reasoning” AI techniques demand more computing power. NVIDIA believes that reasoning will favor its architecture in the long run and expects the inference market to eventually dwarf the training market in size, even if its market share is smaller.
2. AMD
AMD is a fabless chip manufacturer with CPU, GPU, and AI accelerator products.
AMD launched MI300 for AI training workloads in June 2023 and is competing with NVIDIA for market share. There are startups, research institutes, enterprises, and tech giants that have adopted AMD hardware in 2023 since Nvidia AI hardware has been difficult to procure due to rapidly increasing demand, with the rise of generative AI triggered by the launch of ChatGPT.
In 2025, AMD announced the acquisition of a talented team of AI hardware and software engineers from Untether AI, a developer of energy-efficient AI inference chips for edge providers and enterprise data centers. This move enhances AMD’s AI compiler, kernel development, and chip design capabilities, further strengthening its position in the inference market. Additionally, AMD acquired compiler startup Brium to optimize AI performance on its Instinct data center GPUs for enterprise applications.
AMD will be releasing the MI350 series to replace the MI300 and compete with NVIDIA’s H200. AMD claims that MI325X, another recent chip, has market-leading inference performance.
AMD is also working with machine learning companies like Hugging Face to enable data scientists to use their hardware more efficiently.
The software ecosystem is critical as hardware performance relies heavily on software optimization. For example, AMD and NVIDIA had a public disagreement over benchmarking H100 and MI300. The focus of the disagreement was the package and the floating point to use in the benchmark. According to the latest benchmarks, it appears that MI300 is better or on par with H100 for inferencing on a 70B LLM.
Software
While AMD hardware is catching up to NVIDIA, its software lags behind in terms of usability. While CUDA works out of the box for most tasks, AMD software requires significant configuration.
Ecosystem
Like NVIDIA, AMD is selectively investing in users of its solutions to drive adoption of its hardware.
3. Intel
Intel is the most significant player in the CPU market and has a long history of semiconductor development. Unlike NVIDIA and AMD, Intel uses its own foundry to build its chips.
Gaudi3 is the latest AI accelerator processor from Intel. However, Intel’s sales guidance for Gaudi3 was ~$500M for 2024, which is significantly lower compared to the billions that AMD is projecting to earn in 2024.
Intel is experiencing governance issues, as shown by its CEO Pat Gelsinger’s departure in December 2024. A significant share of Intel’s board members lack experience in leading a semiconductor company in an operational manner. Following the departure of its CEO, Intel’s strategy in the AI and foundry markets remains unclear.
Which public cloud providers produce AI chips?
4. AWS
AWS produces Tranium chips for model training and Inferentia chips for inference. Although AWS is the market leader in public cloud, it began developing its own chips after Google.
Hundreds of thousands of Tranium2 chips are used to form the Project Rainier cluster, which powers LLM developer Anthropic’s models.
5. Google Cloud Platform
Google Cloud TPU is the purpose-built machine learning accelerator chip that powers Google products like Translate, Photos, Search, Assistant, and Gmail. It can be used via the Google Cloud as well. Google announced TPUs in 2016.Latest Trillium TPU is the 6th generation.
Google has introduced Ironwood. This latest generation is specifically designed for complex “thinking models” like LLMs and MoEs, offering massive parallel processing (4,614 TFLOPs per chip) and scaling up to 42.5 Exaflops in 9,216-chip pods.
Ironwood delivers significant advancements over Trillium, including 2x better power efficiency, 6x the High Bandwidth Memory capacity (192 GB/chip), 4.5x the HBM bandwidth (7.2 TBps/chip), and 1.5x the Inter-Chip Interconnect speed (1.2 Tbps). It also features an enhanced SparseCore for large embeddings. Google also produces the much smaller Edge TPU for different needs, designed for deployment on edge devices like smartphones and IoT hardware.
6. Alibaba
Alibaba produces chips such as the Hanguang 800 for inference. However, some North American, European, and Australian organizations (e.g., those in the defense industry) may not prefer to use Alibaba Cloud for geopolitical reasons.
7. IBM
IBM announced its latest deep learning chip, the artificial intelligence unit (AIU), in 2022.. IBM is considering using these chips to power its Watsonx generative AI platform.
The IBM AIU builds on the IBM Telum Processor, which powers the AI processing capabilities of IBM Z mainframe servers. At launch, Telum processors’ highlighted use cases included fraud detection.
IBM also demonstrated that merging compute and memory can lead to efficiencies. These were demonstrated in the North Pole processor prototype.
8. Huawei
Huawei’s HiSilicon Ascend 910C is part of the Ascend 910 family of chips introduced in 2019.
Due to sanctions, AI labs in China can not buy the newest, highest-performance chips from US firms like NVIDIA or AMD. Therefore, they are experimenting with Ascend 910C.
Huawei’s cloud is hosting DeepSeek models, and a researcher at DeepSeek claims that it can reach 60% of NVIDIA H100 inference performance.
Which cloud AI providers produce their own chips?
These providers do not have public clouds with comprehensive capabilities like the hyperscalers. They provide limited cloud services, typically focused on AI inference. We were able to sign up for these services without talking to sales teams:
9. Groq
Groq was founded by former Google employees. The company represents LPUs, a new model for AI chip architecture, that aims to make it easier for companies to adopt their systems. The startup has already raised around $350 million and produced its first models, such as GroqChip™ Processor, GroqCard™ Accelerator, etc.
The company is focused on LLM inference and released benchmarks for Llama-2 70B.
Recently, Groq secured a significant $1.5 billion investment commitment from Saudi Arabia to expand the delivery of its advanced AI chips to the country. This investment will be used to expand Groq’s existing data center in Dammam, Saudi Arabia, built in partnership with Aramco Digital.
In Q1 2024, the company shared that 70k developers signed up on its cloud platform and built 19k new applications.
On March 1, 2022, Groq acquired Maxeler, which has high-performance computing (HPC) solutions for financial services.
10. SambaNova Systems
SambaNova Systems was founded in 2017 to develop high-performance, high-precision hardware-software systems for high-volume generative AI workloads. The company has raised more than 1.5 billion dollars in total funding, including a 350 million dollar Series E round in February 2026.
In February 2026, SambaNova unveiled the SN50 chip, its latest Reconfigurable Data Unit (RDU), claiming a max speed 5x faster than competitive chips and 3x lower total cost of ownership compared to GPUs for agentic AI workloads. The SN50 delivers 5x more compute per accelerator and 4x more network bandwidth than the previous generation SN40L, and supports a three-tier memory architecture for 10 trillion+ parameter models and 10 million+ token context lengths.
SoftBank Corp. will be the first customer to deploy SN50 within its next-generation AI data centers in Japan.
SambaNova also announced a planned multi-year strategic collaboration with Intel to deliver AI inference solutions, combining SambaNova’s systems with Intel Xeon processors, Intel GPUs, and Intel networking to power scalable inference infrastructure as an alternative to GPU-centric solutions.
It is important to note that SambaNova Systems also leases its platform to businesses through SambaCloud. This AI platform-as-a-service approach makes their systems easier to adopt and encourages hardware reuse for the circular economy.
Which are the leading AI chip startups?
We would also like to introduce some startups in the AI chip industry whose names we may hear more often in the near future. Even though these companies were founded only recently, they have already raised millions of dollars.
11. Cerebras
Cerebras was founded in 2015 and is the only major chip maker focusing on wafer-scale chips. Wafer-scale chips have advantages in parallelism compared to GPUs, thanks to their higher memory bandwidth. However, designing and manufacturing such chips is an emerging technology.
Cerebras chips include:
- WSE-1 with 1.2 trillion transistors and 400k processing cores.
- WSE-2, with 2.6 trillion transistors and 850k cores, was announced in April 2021. It leveraged TSMC’s 7nm process
- WSE-3, featuring 4 trillion transistors and 900k AI cores, was announced in March 2024. It leverages TSMC’s 5nm process
Celebra’s system works with pharmaceutical companies such as AstraZeneca and GlaxoSmithKline and research labs that rely on it for simulations. It also targets LLM makers since its chips can lower inference costs for frontier models.
Cerebras also offers its chips on its cloud to enterprises.
12. d-Matrix
d-Matrix follows a novel approach, ditching the traditional von Neumann architecture in favor of in-memory computing. While this approach has the potential to resolve the bottleneck between memory and compute, it is a new and yet unproven approach.
13. Rebellions
A Korea-based startup raised $124M in 2024 and is focused on LLM inference.
Rebellions merged with another Korean semiconductor design firm, SAPEON, and reached a unicorn valuation in 2024.
In July 2025, Rebellions secured investment from tech giant Samsung as part of a funding round targeting up to $200 million, ahead of a planned initial public offering (IPO). The company has raised $220 million since its founding in 2020 and is collaborating with Samsung to bring its second-generation chip, Rebel-Quad (comprising four Rebel AI chips), to market later in 2025, leveraging Samsung’s 4-nanometer manufacturing process.
14. Tenstorrent
Tenstorrent’s latest Blackhole Tensix Processor delivers 664 TFLOPS (BLOCKFP8) of performance, paired with 32GB of GDDR6 memory and 512 GB/s memory bandwidth.
The P150a card is priced at $1,399 and features four QSFP-DD 800G ports for multi-card scaling. The entry-level P100a model starts at $999.
Tenstorrent offers a fully open-source software stack. The company raised $700M at a valuation of more than $2.6 billion from investors, including Jeff Bezos, in December 2024.
15. Positron
Positron was founded in 2023 and focuses exclusively on transformer model inference. The company takes an ASIC approach, building purpose-built hardware optimized specifically for transformer architectures rather than general-purpose GPU computing.
Products:
- Atlas (shipping now): A transformer inference server featuring 8x Positron Archer Transformer Accelerators with 256 GB total HBM. The company claims >4x performance per watt and >3x performance per dollar compared to NVIDIA Hopper systems, benchmarked on Llama 3.1 8B with BF16 compute.
- Titan (coming 2027): A next-generation system with 8+ TB memory powered by 4x Asimov custom chips, designed to support up to 16 trillion parameter models and 10 million+ token context windows in an air-cooled 4U form factor.
- Asimov (coming 2027): Custom inference accelerator silicon with 2+ TB memory per chip.
Positron raised a 230M+ Series B round in early 2026 with investors including QIA, Arm Holdings, Arena, and Jump Trading
Atlas is currently used by networking, gaming, content moderation, CDN, and Token-as-a-Service companies. Positron claims its Atlas system demonstrated 3x lower end-to-end latency for trading inference workloads versus comparable H100 systems while consuming one-third of the power.
Positron’s chips are designed, fabricated, and assembled in the United States.
16. _etched
Their approach sacrifices flexibility for efficiency by burning the transformer architecture into their chips.
The team claims
- Sohu has built the world’s first transformer ASIC.
- Those 8 Sohu chips can generate >500,000 tokens/second. This is an order of magnitude more than what 8 NVIDIA B200s can achieve.
Currently, these are based on the team’s internal measurements. AIMultiple teams have not yet come across any benchmarks or client references. We are curious about:
- What happens when the model becomes outdated? Do users need to buy a new chip, or can the old chip be reconfigured with the next model?
- How did they run their benchmark? Which quantization and model were used?
We’ll be updating this as soon as the _etched team releases more details. It will be interesting to see whether burning models to chips will be sustainable, given the release of new models every few months.
17. Taalas
Taalas was founded in early 2023 and takes the most extreme approach to AI chip specialization: hard-wiring individual models directly into custom silicon, producing what the company calls “Hardcore Models.”The company claims it can transform any previously unseen AI model into custom silicon within two months.
Taalas’ architecture unifies storage and compute on a single chip at DRAM-level density, eliminating the need for HBM, advanced packaging, 3D stacking, liquid cooling, or high-speed I/O. The company describes this as a radical simplification of the hardware stack.
Products:
- HC1 (available now): A technology demonstrator hard-wired with Llama 3.1 8B, built on TSMC 6nm with 53 billion transistors. Taalas claims 17,000 tokens per second per user, which it says is nearly 10x faster than the current state of the art, while costing 20x less to build and consuming 10x less power in a 2.5 kW air-cooled server. However, the model uses aggressive custom 3-bit and 6-bit quantization, which introduces quality degradations compared to GPU baselines.
- HC2 (planned): A second-generation platform with higher density, faster execution, and standard 4-bit floating-point formats to address the quantization limitations of HC1.
Taalas has raised more than 200 million dollars but reports spending only 30 million dollars to bring its first product to market with a team of 24 people.
18. Extropic
Extropic raised a $14M round in late 2023 to use thermodynamics for computing. The company hasn’t released a chip yet.
19. Vaire
Vaire is a UK-based startup pioneering reversible computing, an innovative approach that aims to create near-zero energy chips. Unlike traditional computing, where energy is lost as heat, reversible computing recycles a significant portion of energy for subsequent computations.
Vaire has demonstrated a test chip that can recover 50% of its energy, showing the technology’s potential to reduce the energy consumption of AI workloads and circumvent the physical limitations, or thermal wall, that are challenging modern semiconductor manufacturing.
20. Fractile
Fractile is a UK-based AI inference chip startup that emerged from stealth in July 2024 with $15 million in funding to challenge NVIDIA on frontier model inference.
The company is building processors that physically interleave memory and compute on the same die, which it claims solves the simultaneous low-latency and high-throughput requirement that GPUs cannot meet for frontier model inference. Fractile claims its design can run frontier models up to 25x faster and at 1/10th the cost of existing solutions, with the goal of serving thousands of tokens per second to thousands of concurrent users.
Fractile is headquartered in London with hardware engineering in Bristol, and has been profiled by the Financial Times in March 2025 as part of a wave of inference-focused startups challenging NVIDIA’s dominance.
What are the upcoming AI hardware producers?
Though these are compelling AI hardware solutions, there are currently limited benchmarks on their effectiveness since they are newcomers to the market.
21. Apple
Apple’s project ACDC is reported to be focused on building chips for AI inference. Apple is already a major chip designer with its internally designed semiconductors used in iPhones, iPads, and MacBooks.
22. Meta
Meta Training and Inference Accelerator (MTIA) is a family of processors for AI workloads such as training Meta’s LLaMa models.
The latest MTIA model, Next Gen MTIA, is based on TSMC 5nm technology and is claimed to offer 3x the performance of MTIA v1. MTIA will be hosted in racks containing up to 72 accelerators.
MTIA is currently for Meta’s internal usage. However, in the future, if Meta launched an LLaMa-based enterprise generative AI offering, these chips could power such an offering.
23. Microsoft Azure
At Hot Chips 2024, Microsoft unveiled Maia 100, their first custom AI accelerator designed to optimize large-scale AI workloads in Azure through hardware and software co-optimization. Built on TSMC’s N5 process with advanced memory and interconnect technology, Maia 100 targets high throughput and diverse data formats, offering developers flexibility via its SDK for quick deployment of PyTorch and Triton models. However, Microsoft’s next-generation AI chip, code-named Braga, faces delays from 2025 to 2026 due to design changes, staffing constraints, and high turnover, potentially lagging behind Nvidia’s Blackwell chip in power efficiency.
24. OpenAI
OpenAI is finalizing the design of its first AI chip with Broadcom and TSMC using TSMC’s 3-nanometer technology. OpenAI’s chip team’s leadership has experience with designing TPUs at Google, and they aim to have their chip mass-produced in 2026.
What are other AI chip producers?
25. Graphcore
Graphcore is a British company founded in 2016. The company announced its flagship AI chip as IPU-POD256. Graphcore has already been funded with around $700 million.
The company has strategic partnerships with data storage corporations like DDN, Pure Storage, and Vast Data. Graphcore’s AI chips serve research institutes like Oxford-Man Institute of Quantitative Finance, University of Bristol and Berkeley University of California.
The company’s long-term viability was at risk as it was losing ~$200M per year. Graphcore was acquired by SoftBank for over $600 million in October 2024.
26. Mythic
Mythic was founded in 2012 and is focused on edge AI. Mythic follows an unconventional path, an analog compute architecture, that aims to deliver power-efficient edge AI computing.
It has developed products such as M1076 AMP and MM1076 key card, and has already raised about $165 million in funding.
Mythic laid off most of its staff and restructured its business with its funding round in March 2023.
27. Speedata
Founded in 2019 in Tel Aviv, Speedata develops an Analytics Processing Unit (APU) designed to accelerate big data analytics and AI workloads. It’s an APU that targets Apache Spark workloads, with plans to support other major data analytics platforms.
Speedata raised $44M in a Series B round in June 2025, led by Walden Catalyst Ventures, 83North, and others, bringing its total funding to $114M. The company claims its APU outperforms general-purpose processors and GPUs by replacing racks of servers with a single chip, offering superior performance and energy efficiency for data processing.
28. Axelera AI
Founded in July 2021 in Eindhoven, Netherlands, Axelera AI specializes in AI hardware acceleration technology for computer vision and generative AI. The company is developing Titania, an AI inference chiplet based on its Digital In-Memory Computing (D-IMC) architecture, designed to accelerate AI workloads from edge to cloud.
Axelera AI secured up to €61.6 million in funding from the EuroHPC Joint Undertaking (JU) and member states under the DARE Project in March 2025, following a previous $68 million Series B financing round. This brings their total funding to over $200 million in three years. Axelera AI aims to deploy Titania by 2028 to address the increasing demand for high-performance, cost-effective, and sustainable AI solutions, emphasizing its ability to improve throughput and efficiency compared to traditional cloud solutions.
Foundry partners and TSMC’s role
As the world’s leading pure-play foundry, TSMC manufactures semiconductors based on customer designs rather than creating its own chips, distinguishing it from companies like NVIDIA and AMD. While Samsung Foundry and Intel Foundry Services compete in this space, TSMC maintains a technological edge.
Its advanced process technologies, particularly its pioneering 5nm and 3nm nodes, provide the essential combination of performance and energy efficiency required for cutting-edge AI applications, as shown in its manufacturing partnerships with the AI chip designers listed below:
Expansion plans
TSMC is seeking Nvidia, AMD, Broadcom, and Qualcomm to invest in a joint venture to run Intel’s foundry division, retaining operational control but less than 50% ownership. This initiative, backed by the Trump administration, comes after TSMC announced plans for a significant U.S. investment and aims to revive Intel and strengthen U.S. chip manufacturing. The deal faces challenges due to process differences but builds on TSMC’s strengths as a leading foundry.
What are the AI chip makers in China?
Due to US sanctions preventing many Chinese companies from acquiring the most advanced AI chips from AMD and NVIDIA, Chinese buyers have increased their purchases from local producers.
Other than Huawei and Alibaba covered above, these are the leading AI chip producers in China:
- Cambricon focuses on AI hardware and expects ~$150M in sales in its latest year of operations.
- Baidu is using Kunlun chips in its cloud and is designing the 3rd generation chip. Kunlun 2 was comparable to NVIDIA A100.
- Biren, founded by NVIDIA alumni, produces BR106 & BR110 GPU chips.
- Moore Threads produces MTT S2000 GPUs.
FAQs
Chips and the equipment that builds them are the most complex machines ever built by humans. Though there are many companies in the semiconductor ecosystem, we focused on chip designers like NVIDIA in this article.
Most chip designers outsource chip manufacturing to foundries like TSMC. Foundries use lithography equipment produced by companies like ASML to manufacture these chips. The ecosystem is supported by providers like Arm and Synopsys that supply IP and design tools.
As seen above, an increasing number of parameters, dataset size, and compute led generative AI models to become more accurate. To build better deep learning models and power generative AI applications, organizations require increased computing power and memory bandwidth.
Powerful general-purpose chips (such as CPUs) cannot support highly parallelized deep learning models. Therefore, AI chips (e.g., GPUs) that enable parallel computing capabilities are increasingly in demand.
Hyperscalers are responding to this by designing their own chips, a process that takes years. The rest need to follow one of these routes to build their own AI models: Rent capacity from cloud GPU providers or buy hardware from the top AI chip vendors listed in this article.
AI hardware is also called neural processing units (NPUs), AI accelerators, or deep learning processors (DLPs).
Further reading
For hands-on performance comparisons of the chips covered in this article, see our benchmarks:
- Multi-GPU benchmark: How NVIDIA’s B200, H200, H100, and AMD’s MI300X scale across 1, 2, 4, and 8-GPU configurations for LLM inference, with throughput, latency, and cost-per-token analysis.
- GPU concurrency benchmark: How NVIDIA’s B200, H200, H100, and AMD’s MI300X handle 1 to 512 concurrent requests, including system throughput, per-query speed, end-to-end latency, and tokens-per-dollar at each concurrency level.
Cem Dilmegani
Principal Analyst
Cem’s work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem’s work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
View Full Profile
